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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 411–420 of 1808 papers

Title	Date	Tasks	Status	Hype
Capsule Neural Networks as Noise Stabilizer for Time Series Data	Mar 20, 2024	Adversarial AttackTime Series	—Unverified	0
Chain Association-based Attacking and Shielding Natural Language Processing Systems	Nov 12, 2024	Adversarial Attack	—Unverified	0
Automated Decision-based Adversarial Attacks	May 9, 2021	Adversarial AttackProgram Synthesis	—Unverified	0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks	Aug 22, 2024	Adversarial AttackMulti-Object Tracking	—Unverified	0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks	Dec 21, 2023	Adversarial Attack	—Unverified	0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack	Mar 18, 2022	Adversarial AttackNetwork Pruning	—Unverified	0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification	Jan 3, 2025	Adversarial AttackMeta-Learning	—Unverified	0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization	May 9, 2024	Adversarial AttackDeep Learning	—Unverified	0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality	Mar 12, 2023	Adversarial Attack	—Unverified	0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective	Feb 19, 2023	Adversarial AttackBackdoor Attack	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified